Using of Hydrological and Climatic Modeling to Estimate Future Runoff Reaching the Euphrates River from Hiqlan Valley
Bibliographic record
Abstract
In this study, a hydrological model and a statistical weather generation model were used to estimate future surface runoff and generate future weather elements for the Hiqlan Valley, located in the desert of the Iraqi western region.Using the climate model (LARS.WG), the weather data for the past ten years were used to generate weather data for the following ten years.The results of this model showed that maximum precipitation occurs in January, March, and December each year.These data were used as input weather data for the hydrological model: Soil and Water Assessment Tool (SWAT).Delineation of the watershed performed in the SWAT modelling yielded 17 sub-basins and 78 hydrological response units.This model simulates data representing the expected surface runoff for the next ten years for Hiqlan Valley, which reaches directly towards the Euphrates River at an outflow location (15 km downstream) from d/s of Hadith dam as an additional amount of water in the rainy season.The simulation results from the SWAT simulation showed that maximum runoff occurs in January (2029) at 12.3 mm, November (2031) at 15.1 mm, and December (2030) at 13.2 mm in the winter season, and March (2034) at 14.7 mm, and April (2028) at 22.8 mm in the spring season.The runoff occurs at a rate of two to four times annually.Estimating the amount of future surface runoff for Hiqlan Valley is necessary for planning and managing the water resources of the Euphrates River basin, which has been suffering from a recent shortage of water supplies in recent years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".